{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning for Trading (with Python)\n",
    "\n",
    "Notebook for the \"**Machine Learning for Trading with Python**\" webinar done for futures.io.\n",
    "\n",
    "&copy; Ran Aroussi | [@aroussi](http://twitter.com/aroussi) | [aroussi.com](http://aroussi.com) | [github.com/ranaroussi](https://github.com/ranaroussi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline\n",
    "\n",
    "# import modules\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "pd.set_option('notebook_repr_html', True)\n",
    "pd.set_option('expand_frame_repr', False)\n",
    "\n",
    "sns.set(font_scale=1.2, rc={\n",
    "    'figure.figsize': (10, 7),\n",
    "    'axes.facecolor':'white',\n",
    "    'figure.facecolor':'white',\n",
    "    'grid.color':'#dddddd',\n",
    "    'grid.linewidth': 0.25,\n",
    "    \"lines.linewidth\": 1.5,\n",
    "    'text.color': 'gray',\n",
    "    'xtick.color': 'gray',\n",
    "    'ytick.color': 'gray'\n",
    "})\n",
    "# flat ui colors\n",
    "flatui = [\"#af4b64\", \"#3399cc\", \"#4fa487\", \"#9b59b6\", \"#95a5a6\", \n",
    "          \"#ee8659\", \"#4f6c86\", \"#254a82\", \"#c5a06e\", \"#cbb1b8\"]\n",
    "sns.set_palette(flatui)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Machine Learning Algorithms\n",
    "\n",
    "- **Unupervised Learning:** based on input\n",
    "- **Supervised Learning:** based on input + output\n",
    "- **Reinforced Learning:** based on feedback\n",
    "- **Deep Learning:** based on nothing :)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# The Machine Learning Workflow\n",
    "\n",
    "1. Data Gathering + Cleaning\n",
    "- Feature Engineering + Normalization\n",
    "- Model Selection\n",
    "- Split Data\n",
    "- Train Data\n",
    "- Test Data\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Feature Engineering ≈ Alpha Factors\n",
    "\n",
    "<big><br>Features are attributes that holds some predictive power over the end result</big>\n",
    "\n",
    "**Examples:**\n",
    "\n",
    "- Previous _N_ days returns\n",
    "- Inter-market relationships\n",
    "- Technical indicators\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Model Selection\n",
    "\n",
    "<a href=\"http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html\">\n",
    "<img style=\"max-height:400px!important;padding:0 20px;margin:20px auto 0;\" src=\"http://aroussi.com/assets/img/ml-map.jpg\"></a>\n",
    "\n",
    "Source: [scikit-learn.org](http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Machine Learning for Trading\n",
    "\n",
    "<img src=\"http://aroussi.com/assets/img/ml-sl.png\" style=\"max-width:80%!important;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# ML Algorithms for \"Beginners\"\n",
    "\n",
    "<big><br>The two ML algorithems that are easiest to get started with are <strong>Decision Trees</strong> and <strong>K Nearest-Neighbours</strong>. They are both easy to comprehend and works for either Regression or Classification.</big>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## KNN (K Nearest-Neighbours)\n",
    "\n",
    "- Training is fast\n",
    "- Query is slow\n",
    "- Requires data normalization\n",
    "- Needs features\n",
    "\n",
    "<img src=\"http://houseofbots.com/images/news/2542/cover.png\" style=\"max-width:80%!important;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Decision Trees\n",
    "\n",
    "- Training is slow\n",
    "- Query is fast\n",
    "- No need for data normalization\n",
    "- Auto-discover features\n",
    "\n",
    "<img src=\"http://aroussi.com/assets/img/ml-dt.png\" style=\"max-width:90%!important;\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Example: Predicting Tomorrow's Direction using a Decision Tree Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import fix_yahoo_finance as yf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Get Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPX</th>\n",
       "      <th>VIX</th>\n",
       "      <th>VXV</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-09-14</th>\n",
       "      <td>2904.979980</td>\n",
       "      <td>12.07</td>\n",
       "      <td>14.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-17</th>\n",
       "      <td>2888.800049</td>\n",
       "      <td>13.68</td>\n",
       "      <td>15.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-18</th>\n",
       "      <td>2904.310059</td>\n",
       "      <td>12.79</td>\n",
       "      <td>15.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-19</th>\n",
       "      <td>2907.949951</td>\n",
       "      <td>11.75</td>\n",
       "      <td>14.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-20</th>\n",
       "      <td>2932.149902</td>\n",
       "      <td>11.52</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SPX    VIX    VXV\n",
       "Date                                 \n",
       "2018-09-14  2904.979980  12.07  14.37\n",
       "2018-09-17  2888.800049  13.68  15.28\n",
       "2018-09-18  2904.310059  12.79  15.04\n",
       "2018-09-19  2907.949951  11.75  14.65\n",
       "2018-09-20  2932.149902  11.52    NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symbols = [\"^GSPC\", \"^VIX\", \"^VXV\"]\n",
    "raw = pd.read_pickle('ml-raw-data.pkl')\n",
    "# raw = yf.download(symbols, start='1990-01-01', auto_adjust=True, group_by=\"symbol\")\n",
    "\n",
    "df = pd.DataFrame()\n",
    "for symbol in symbols:\n",
    "    new_symbol = symbol.replace(\"^\", \"\").replace(\"GSPC\", \"SPX\")\n",
    "    df[new_symbol] = raw[symbol]['Close']\n",
    "\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Prepare Features (input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPX</th>\n",
       "      <th>VIX</th>\n",
       "      <th>VXV</th>\n",
       "      <th>SPXVOL</th>\n",
       "      <th>SPX1D</th>\n",
       "      <th>VIX1D</th>\n",
       "      <th>VXV1D</th>\n",
       "      <th>SPXHL</th>\n",
       "      <th>SPXOC</th>\n",
       "      <th>SPXO2C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-09-13</th>\n",
       "      <td>2904.179932</td>\n",
       "      <td>12.37</td>\n",
       "      <td>14.77</td>\n",
       "      <td>-0.306285</td>\n",
       "      <td>0.528225</td>\n",
       "      <td>-5.859970</td>\n",
       "      <td>-3.590078</td>\n",
       "      <td>0.460205</td>\n",
       "      <td>-7.329834</td>\n",
       "      <td>0.253028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-14</th>\n",
       "      <td>2904.979980</td>\n",
       "      <td>12.07</td>\n",
       "      <td>14.37</td>\n",
       "      <td>-3.229870</td>\n",
       "      <td>0.027548</td>\n",
       "      <td>-2.425222</td>\n",
       "      <td>-2.708192</td>\n",
       "      <td>10.609863</td>\n",
       "      <td>1.399903</td>\n",
       "      <td>-0.048167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-17</th>\n",
       "      <td>2888.800049</td>\n",
       "      <td>13.68</td>\n",
       "      <td>15.28</td>\n",
       "      <td>-6.414376</td>\n",
       "      <td>-0.556972</td>\n",
       "      <td>13.338857</td>\n",
       "      <td>6.332637</td>\n",
       "      <td>17.670166</td>\n",
       "      <td>15.030029</td>\n",
       "      <td>-0.517593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-18</th>\n",
       "      <td>2904.310059</td>\n",
       "      <td>12.79</td>\n",
       "      <td>15.04</td>\n",
       "      <td>4.303268</td>\n",
       "      <td>0.536901</td>\n",
       "      <td>-6.505848</td>\n",
       "      <td>-1.570681</td>\n",
       "      <td>0.310058</td>\n",
       "      <td>-13.570069</td>\n",
       "      <td>0.469432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-19</th>\n",
       "      <td>2907.949951</td>\n",
       "      <td>11.75</td>\n",
       "      <td>14.65</td>\n",
       "      <td>6.680847</td>\n",
       "      <td>0.125327</td>\n",
       "      <td>-8.131353</td>\n",
       "      <td>-2.593085</td>\n",
       "      <td>2.780030</td>\n",
       "      <td>-1.349853</td>\n",
       "      <td>0.046441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SPX    VIX    VXV    SPXVOL     SPX1D      VIX1D     VXV1D      SPXHL      SPXOC    SPXO2C\n",
       "Date                                                                                                          \n",
       "2018-09-13  2904.179932  12.37  14.77 -0.306285  0.528225  -5.859970 -3.590078   0.460205  -7.329834  0.253028\n",
       "2018-09-14  2904.979980  12.07  14.37 -3.229870  0.027548  -2.425222 -2.708192  10.609863   1.399903 -0.048167\n",
       "2018-09-17  2888.800049  13.68  15.28 -6.414376 -0.556972  13.338857  6.332637  17.670166  15.030029 -0.517593\n",
       "2018-09-18  2904.310059  12.79  15.04  4.303268  0.536901  -6.505848 -1.570681   0.310058 -13.570069  0.469432\n",
       "2018-09-19  2907.949951  11.75  14.65  6.680847  0.125327  -8.131353 -2.593085   2.780030  -1.349853  0.046441"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SPXVOL'] = raw['^GSPC']['Volume'].pct_change() * 100\n",
    "df['SPX1D']  = raw['^GSPC']['Close'].pct_change() * 100\n",
    "df['VIX1D']  = raw['^VIX']['Close'].pct_change() * 100\n",
    "df['VXV1D']  = raw['^VXV']['Close'].pct_change() * 100\n",
    "\n",
    "df['SPXHL']  = raw['^GSPC']['Open'] - raw['^GSPC']['Low']\n",
    "df['SPXOC']  = raw['^GSPC']['Open'] - raw['^GSPC']['Close']\n",
    "df['SPXO2C'] = ((raw['^GSPC']['Close'] / raw['^GSPC']['Open']) - 1) * 100\n",
    "\n",
    "df.dropna(inplace=True)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Prepare target (output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPX</th>\n",
       "      <th>VIX</th>\n",
       "      <th>VXV</th>\n",
       "      <th>SPXVOL</th>\n",
       "      <th>SPX1D</th>\n",
       "      <th>VIX1D</th>\n",
       "      <th>VXV1D</th>\n",
       "      <th>SPXHL</th>\n",
       "      <th>SPXOC</th>\n",
       "      <th>SPXO2C</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-09-05</th>\n",
       "      <td>2888.600098</td>\n",
       "      <td>13.91</td>\n",
       "      <td>15.92</td>\n",
       "      <td>5.335938</td>\n",
       "      <td>-0.280313</td>\n",
       "      <td>5.699088</td>\n",
       "      <td>3.042071</td>\n",
       "      <td>14.670166</td>\n",
       "      <td>2.989990</td>\n",
       "      <td>-0.103403</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-06</th>\n",
       "      <td>2878.050049</td>\n",
       "      <td>14.65</td>\n",
       "      <td>16.34</td>\n",
       "      <td>-3.136444</td>\n",
       "      <td>-0.365231</td>\n",
       "      <td>5.319914</td>\n",
       "      <td>2.638191</td>\n",
       "      <td>21.349854</td>\n",
       "      <td>10.589844</td>\n",
       "      <td>-0.366603</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-07</th>\n",
       "      <td>2871.679932</td>\n",
       "      <td>14.88</td>\n",
       "      <td>16.59</td>\n",
       "      <td>-6.157492</td>\n",
       "      <td>-0.221334</td>\n",
       "      <td>1.569966</td>\n",
       "      <td>1.529988</td>\n",
       "      <td>4.139893</td>\n",
       "      <td>-3.419922</td>\n",
       "      <td>0.119233</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-10</th>\n",
       "      <td>2877.129883</td>\n",
       "      <td>14.16</td>\n",
       "      <td>16.17</td>\n",
       "      <td>-7.292950</td>\n",
       "      <td>0.189783</td>\n",
       "      <td>-4.838710</td>\n",
       "      <td>-2.531646</td>\n",
       "      <td>5.449952</td>\n",
       "      <td>4.260010</td>\n",
       "      <td>-0.147846</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-11</th>\n",
       "      <td>2887.889893</td>\n",
       "      <td>13.22</td>\n",
       "      <td>15.54</td>\n",
       "      <td>6.160211</td>\n",
       "      <td>0.373984</td>\n",
       "      <td>-6.638418</td>\n",
       "      <td>-3.896104</td>\n",
       "      <td>4.790039</td>\n",
       "      <td>-16.319825</td>\n",
       "      <td>0.568324</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-12</th>\n",
       "      <td>2888.919922</td>\n",
       "      <td>13.14</td>\n",
       "      <td>15.32</td>\n",
       "      <td>12.596994</td>\n",
       "      <td>0.035667</td>\n",
       "      <td>-0.605144</td>\n",
       "      <td>-1.415701</td>\n",
       "      <td>9.090088</td>\n",
       "      <td>-0.629883</td>\n",
       "      <td>0.021808</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-13</th>\n",
       "      <td>2904.179932</td>\n",
       "      <td>12.37</td>\n",
       "      <td>14.77</td>\n",
       "      <td>-0.306285</td>\n",
       "      <td>0.528225</td>\n",
       "      <td>-5.859970</td>\n",
       "      <td>-3.590078</td>\n",
       "      <td>0.460205</td>\n",
       "      <td>-7.329834</td>\n",
       "      <td>0.253028</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-14</th>\n",
       "      <td>2904.979980</td>\n",
       "      <td>12.07</td>\n",
       "      <td>14.37</td>\n",
       "      <td>-3.229870</td>\n",
       "      <td>0.027548</td>\n",
       "      <td>-2.425222</td>\n",
       "      <td>-2.708192</td>\n",
       "      <td>10.609863</td>\n",
       "      <td>1.399903</td>\n",
       "      <td>-0.048167</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-17</th>\n",
       "      <td>2888.800049</td>\n",
       "      <td>13.68</td>\n",
       "      <td>15.28</td>\n",
       "      <td>-6.414376</td>\n",
       "      <td>-0.556972</td>\n",
       "      <td>13.338857</td>\n",
       "      <td>6.332637</td>\n",
       "      <td>17.670166</td>\n",
       "      <td>15.030029</td>\n",
       "      <td>-0.517593</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-18</th>\n",
       "      <td>2904.310059</td>\n",
       "      <td>12.79</td>\n",
       "      <td>15.04</td>\n",
       "      <td>4.303268</td>\n",
       "      <td>0.536901</td>\n",
       "      <td>-6.505848</td>\n",
       "      <td>-1.570681</td>\n",
       "      <td>0.310058</td>\n",
       "      <td>-13.570069</td>\n",
       "      <td>0.469432</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SPX    VIX    VXV     SPXVOL     SPX1D      VIX1D     VXV1D      SPXHL      SPXOC    SPXO2C  target\n",
       "Date                                                                                                                   \n",
       "2018-09-05  2888.600098  13.91  15.92   5.335938 -0.280313   5.699088  3.042071  14.670166   2.989990 -0.103403    -1.0\n",
       "2018-09-06  2878.050049  14.65  16.34  -3.136444 -0.365231   5.319914  2.638191  21.349854  10.589844 -0.366603     1.0\n",
       "2018-09-07  2871.679932  14.88  16.59  -6.157492 -0.221334   1.569966  1.529988   4.139893  -3.419922  0.119233    -1.0\n",
       "2018-09-10  2877.129883  14.16  16.17  -7.292950  0.189783  -4.838710 -2.531646   5.449952   4.260010 -0.147846     1.0\n",
       "2018-09-11  2887.889893  13.22  15.54   6.160211  0.373984  -6.638418 -3.896104   4.790039 -16.319825  0.568324     1.0\n",
       "2018-09-12  2888.919922  13.14  15.32  12.596994  0.035667  -0.605144 -1.415701   9.090088  -0.629883  0.021808     1.0\n",
       "2018-09-13  2904.179932  12.37  14.77  -0.306285  0.528225  -5.859970 -3.590078   0.460205  -7.329834  0.253028    -1.0\n",
       "2018-09-14  2904.979980  12.07  14.37  -3.229870  0.027548  -2.425222 -2.708192  10.609863   1.399903 -0.048167    -1.0\n",
       "2018-09-17  2888.800049  13.68  15.28  -6.414376 -0.556972  13.338857  6.332637  17.670166  15.030029 -0.517593     1.0\n",
       "2018-09-18  2904.310059  12.79  15.04   4.303268  0.536901  -6.505848 -1.570681   0.310058 -13.570069  0.469432     1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'] = np.where(df[\"SPXO2C\"] >= 0, 1, np.where(df[\"SPXO2C\"] < 0, -1, 0))\n",
    "df['target'] = df['target'].shift(-1) # next day\n",
    "\n",
    "df.dropna(inplace=True)\n",
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Split Data into training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols = [col for col in df.columns if col not in ['target']]\n",
    "\n",
    "features = df[feature_cols].values\n",
    "labels   = df['target'].values.flatten()\n",
    "\n",
    "# split df\n",
    "train_test_split = .9\n",
    "sample = int( len(df.index) * train_test_split )\n",
    "\n",
    "train_features = features[:-sample]\n",
    "train_labels   = labels[:-sample]\n",
    "\n",
    "test_features = features[-sample:]\n",
    "test_labels   = labels[-sample:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Normalize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize\n",
    "normalizer = preprocessing.Normalizer()\n",
    "train_features = normalizer.fit(train_features).transform(train_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Do ML Stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# init classifier\n",
    "clf = DecisionTreeClassifier()\n",
    "\n",
    "# fit \n",
    "clf.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5395213923132705"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict\n",
    "prediction = clf.predict(test_features)\n",
    "\n",
    "# score\n",
    "accuracy_score(test_labels, prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean 50.96%, Std: 2.77\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(clf, features, labels, cv=10)\n",
    "print(\"Mean %.2f%%, Std: %.2f\" % (scores.mean()*100, scores.std()*100) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Test Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPX</th>\n",
       "      <th>VIX</th>\n",
       "      <th>VXV</th>\n",
       "      <th>SPXVOL</th>\n",
       "      <th>SPX1D</th>\n",
       "      <th>VIX1D</th>\n",
       "      <th>VXV1D</th>\n",
       "      <th>SPXHL</th>\n",
       "      <th>SPXOC</th>\n",
       "      <th>SPXO2C</th>\n",
       "      <th>target</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-09-12</th>\n",
       "      <td>2888.919922</td>\n",
       "      <td>13.14</td>\n",
       "      <td>15.32</td>\n",
       "      <td>12.596994</td>\n",
       "      <td>0.035667</td>\n",
       "      <td>-0.605144</td>\n",
       "      <td>-1.415701</td>\n",
       "      <td>9.090088</td>\n",
       "      <td>-0.629883</td>\n",
       "      <td>0.021808</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-13</th>\n",
       "      <td>2904.179932</td>\n",
       "      <td>12.37</td>\n",
       "      <td>14.77</td>\n",
       "      <td>-0.306285</td>\n",
       "      <td>0.528225</td>\n",
       "      <td>-5.859970</td>\n",
       "      <td>-3.590078</td>\n",
       "      <td>0.460205</td>\n",
       "      <td>-7.329834</td>\n",
       "      <td>0.253028</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-14</th>\n",
       "      <td>2904.979980</td>\n",
       "      <td>12.07</td>\n",
       "      <td>14.37</td>\n",
       "      <td>-3.229870</td>\n",
       "      <td>0.027548</td>\n",
       "      <td>-2.425222</td>\n",
       "      <td>-2.708192</td>\n",
       "      <td>10.609863</td>\n",
       "      <td>1.399903</td>\n",
       "      <td>-0.048167</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-17</th>\n",
       "      <td>2888.800049</td>\n",
       "      <td>13.68</td>\n",
       "      <td>15.28</td>\n",
       "      <td>-6.414376</td>\n",
       "      <td>-0.556972</td>\n",
       "      <td>13.338857</td>\n",
       "      <td>6.332637</td>\n",
       "      <td>17.670166</td>\n",
       "      <td>15.030029</td>\n",
       "      <td>-0.517593</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-18</th>\n",
       "      <td>2904.310059</td>\n",
       "      <td>12.79</td>\n",
       "      <td>15.04</td>\n",
       "      <td>4.303268</td>\n",
       "      <td>0.536901</td>\n",
       "      <td>-6.505848</td>\n",
       "      <td>-1.570681</td>\n",
       "      <td>0.310058</td>\n",
       "      <td>-13.570069</td>\n",
       "      <td>0.469432</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SPX    VIX    VXV     SPXVOL     SPX1D      VIX1D     VXV1D      SPXHL      SPXOC    SPXO2C  target  predicted\n",
       "Date                                                                                                                              \n",
       "2018-09-12  2888.919922  13.14  15.32  12.596994  0.035667  -0.605144 -1.415701   9.090088  -0.629883  0.021808     1.0        1.0\n",
       "2018-09-13  2904.179932  12.37  14.77  -0.306285  0.528225  -5.859970 -3.590078   0.460205  -7.329834  0.253028    -1.0        1.0\n",
       "2018-09-14  2904.979980  12.07  14.37  -3.229870  0.027548  -2.425222 -2.708192  10.609863   1.399903 -0.048167    -1.0       -1.0\n",
       "2018-09-17  2888.800049  13.68  15.28  -6.414376 -0.556972  13.338857  6.332637  17.670166  15.030029 -0.517593     1.0        1.0\n",
       "2018-09-18  2904.310059  12.79  15.04   4.303268  0.536901  -6.505848 -1.570681   0.310058 -13.570069  0.469432     1.0        1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdf = df[-sample:].copy()\n",
    "testdf['predicted'] = prediction\n",
    "\n",
    "testdf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f1b3898>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 411,
       "width": 607
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "testdf['strategy'] = testdf['predicted'].shift(1) * testdf['SPXO2C']\n",
    "\n",
    "testdf[['SPX1D', 'strategy']].cumsum().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# and... We're Done :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "This was the part 4 out of the 4-part webinar series\n",
    "\n",
    "- <strike>Prototyping Trading Strategies</strike>\n",
    "- <strike>Backtesting & Optimization</strike>\n",
    "- <strike>Live Trading</strike>\n",
    "- <strike>Using Machine Learning in Trading</strike>\n",
    "\n",
    "**Webinars available @ [aroussi.com/webinars](https://aroussi.com/webinars)**\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Further Reading\n",
    "\n",
    "- [r2d3.us](http://www.r2d3.us)\n",
    "- [It’s Been a Rough 2018 for Many Quant Hedge Funds (Bloomberg)](https://www.bloomberg.com/news/articles/2018-06-26/quants-beset-by-woes-as-jpmorgan-pulls-money-from-hedge-fund)\n",
    "- [Why is machine learning in finance so hard (video)](https://www.youtube.com/watch?v=Szzp6pe4cns)\n",
    "- [Coursera](https://www.coursera.org/learn/machine-learning) / [Udacity](https://www.udacity.com/school-of-ai)\n",
    "- The Intraweb :)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Machine Learning for Trading (with Python)\n",
    "\n",
    "Notebook for the \"**Machine Learning for Trading with Python**\" webinar done for futures.io.\n",
    "\n",
    "&copy; Ran Aroussi | [@aroussi](http://twitter.com/aroussi) | [aroussi.com](http://aroussi.com) | [github.com/ranaroussi](https://github.com/ranaroussi)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
